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Entirely noninvasive outcome prediction in central nervous system lymphomas using circulating tumor DNA.
- Source :
-
Blood [Blood] 2024 Feb 08; Vol. 143 (6), pp. 522-534. - Publication Year :
- 2024
-
Abstract
- Abstract: State-of-the-art response assessment of central nervous system lymphoma (CNSL) by magnetic resonance imaging is challenging and an insufficient predictor of treatment outcomes. Accordingly, the development of novel risk stratification strategies in CNSL is a high unmet medical need. We applied ultrasensitive circulating tumor DNA (ctDNA) sequencing to 146 plasma and cerebrospinal fluid (CSF) samples from 67 patients, aiming to develop an entirely noninvasive dynamic risk model considering clinical and molecular features of CNSL. Our ultrasensitive method allowed for the detection of CNSL-derived mutations in plasma ctDNA with high concordance to CSF and tumor tissue. Undetectable plasma ctDNA at baseline was associated with favorable outcomes. We tracked tumor-specific mutations in plasma-derived ctDNA over time and developed a novel CNSL biomarker based on this information: peripheral residual disease (PRD). Persistence of PRD after treatment was highly predictive of relapse. Integrating established baseline clinical risk factors with assessment of radiographic response and PRD during treatment resulted in the development and independent validation of a novel tool for risk stratification: molecular prognostic index for CNSL (MOP-C). MOP-C proved to be highly predictive of outcomes in patients with CNSL (failure-free survival hazard ratio per risk group of 6.60; 95% confidence interval, 3.12-13.97; P < .0001) and is publicly available at www.mop-c.com. Our results highlight the role of ctDNA sequencing in CNSL. MOP-C has the potential to improve the current standard of clinical risk stratification and radiographic response assessment in patients with CNSL, ultimately paving the way toward individualized treatment.<br /> (© 2024 American Society of Hematology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)
Details
- Language :
- English
- ISSN :
- 1528-0020
- Volume :
- 143
- Issue :
- 6
- Database :
- MEDLINE
- Journal :
- Blood
- Publication Type :
- Academic Journal
- Accession number :
- 37946299
- Full Text :
- https://doi.org/10.1182/blood.2023022020